发表机构
Basque Center for Applied Mathematics (BCAM); College of Semiconductor (College of Integrated Circuits), Hunan University; College of Computer Science, Beijing Information Science and Technology University; Beijing National Research Center for Information Science and Technology; Shanghai AI Laboratory（巴斯克应用数学中心; 湖南大学半导体学院（集成电路学院）; 北京信息科技大学计算机学院; 北京信息科学与技术国家研究中心; 上海人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出IIns-VAE+，结合IIns-VAE与极小极大风险分类器，提升无线感知环境识别在迁移学习中的鲁棒性，实验验证其显著优于基线。
AI 中文摘要
无线感知中的环境识别对于6G集成感知与通信（ISAC）系统实现可靠的情境感知至关重要。然而，用于该任务的深度学习（DL）模型在跨不同环境的域偏移下往往难以泛化。尽管实例间变分自编码器（IIns-VAE）能够学习具有丰富表示的特征，但其神经分类器仍易受这些分布变化的影响。在本文中，我们提出了IIns-VAE+，一种将IIns-VAE框架与极小极大风险分类器（MRC）相结合的混合模型，以提高迁移学习场景中的适应性。我们使用真实世界数据集在三个迁移学习场景中评估我们的框架，包括从一般到特定房间环境、从高到低标签分辨率以及从混合到特定环境。实验结果表明，IIns-VAE+显著优于基线模型，证明了其在构建未来6G系统中自适应且鲁棒的感知网络方面的关键价值。
英文摘要
Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situational awareness. However, deep learning (DL) models for this task often fail to generalize under domain shift across diverse environments. While the Inter-Instance Variational Auto-encoder (IIns-VAE) learns features of rich representation, its neural classifier remains vulnerable to these distribution changes. In this paper, we propose IIns-VAE+, a hybrid model that combines the IIns-VAE framework with Minimax Risk Classifiers (MRC) to improve adaptability in transfer learning scenarios. We use real-world datasets to evaluate our framework across three transfer learning scenarios, including general to specific room environments, high to low label resolutions, and mixed to specific environments. The experimental results indicate that IIns-VAE+ significantly outperforms baselines, demonstrating its critical value in building adaptable and robust perceptive networks in future 6G systems.
Comments17 pages
